Questools Introduction

Elyas Heidari

0.1 Installation and import

Questools can be installed through github:

# devtools::install_github("bAIo-lab/Questools")
library(Questools)

0.2 Dateset

We’ll use a health Questionnaire dataset to work with Questools. The dataset contains 291 variables, including 36 continuous (i.e., laboratory measurements), and 255 categorical variables (i.e., questions) having no missing data.

data <- read.csv("../data/data.csv")
kableExtra::kable(head(data)) %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "200px")

Age	Vazn	Ghad	BMI	MetaP	CharbiE	FesharS	FesharD	Nabz	DarsadChB	DarsadAzB	DorBadan	DorBasan	DorGardan	FSG	Urea	UAC	Chol	TG	Crea	LDH	CBC	WBC	RBC	HB	Hct	MCV	MCH	MCHC	Platelet	Lymph	Mxd	Neut	RDW	PDW	MPV	test1	test2	test3	test4	test5	test6	test7	test8	test9	test10	test11	test12	test13	test14	test15	test16	test17	test18	test19	test20	test21	test22	test23	test24	test25	test26	test27	test28	test29	test30	test31	test32	test33	test34	test35	test36	test37	test38	test39	test40	test41	test42	test43	test44	test45	test46	test47	test48	test49	test50	test51	test52	test53	test54	test55	test56	test57	test58	test59	test60	test61	test62	test63	test64	test65	test66	test67	test68	test69	test70	test71	test72	test73	test74	test75	test76	test77	test78	test79	test80	test81	test82	test83	test84	test85	test86	test87	test88	test89	test90	test91	test92	test93	test94	test95	test96	test97	test98	test99	test100	test101	test102	test103	test104	test105	test106	test107	test108	test109	test110	test111	test112	test113	test114	test115	test116	test117	test118	test119	test120	test121	test122	test123	test124	test125	test126	test127	test128	test129	test130	test131	test132	test133	test134	test135	test136	test137	test138	test139	test140	test141	test142	test143	test144	test145	test146	test147	test148	test149	test150	test151	test152	test153	test154	test155	test156	test157	test158	test159	test160	test161	test162	test163	test164	test165	test166	test167	test168	test169	test170	test171	test172	test173	test174	test175	test176	test177	test178	test179	test180	test181	test182	test183	test184	test185	test186	test187	test188	test189	test190	test191	test192	test193	test194	test195	test196	test197	test198	test199	test200	test201	test202	test203	test204	test205	test206	test207	test208	test209	test210	test211	test212	test213	test214	test215	test216	test217	test218
55	114.7	163	43.0	1868	15	137	76	62	55.0	20.0	121	149	37	129.0000	38.00000	4.100000	250.0000	131.0000	0.700000	75.00000	5416.166	6300.000	4.85	13.80	41.3	85.2	28.5	33.4	268000.0	38.1	10.1	51.8	13.5	11.1	8.8	4	2	5	1	1	1	1	1	3	2	5	1	5	1	4	4	5	2	1	2	3	5	4	4	4	3	2	5	5	5	1	1	4	4	3	1	4	2	4	2	2	2	2	2	3	1	2	3	2	3	1	1	1	3	4	4	4	4	3	3	4	1	2	4	1	1	1	1	1	2	1	4	1	1	2	2	2	1	2	1	4	3	1	4	1	2	5	2	1	4	1	1	2	2	2	2	2	2	5	2	2	2	1	5	2	2	1	4	1	1	2	2	2	2	2	5	2	5	2	1	1	4	5	1	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	1	1	2	2	2	2	5	4	3	5	1	2	1	5	3	5	1	5	5	1	1	1	4	1	1	1	5	1	1	1	2	2	5	5	5	4	5	2	2	3	4	2	1	2	5	4	1	1
57	90.8	168	24.2	1842	18	138	88	72	32.9	30.2	47	117	42	102.0000	34.00000	7.300000	306.0000	123.0000	1.000000	46.00000	241.100	6166.572	6200.00	5.37	14.0	42.5	79.1	26.1	32.9	156000.0	38.4	12.4	49.2	13.9	12.3	4	1	5	1	1	2	1	1	3	3	2	1	5	1	4	2	3	2	2	3	1	1	1	2	1	1	1	5	4	2	1	2	1	3	1	1	2	2	1	2	3	1	1	1	2	1	2	3	3	1	1	1	1	3	1	2	1	3	1	2	1	2	3	5	2	1	1	1	1	1	2	5	2	2	2	2	2	1	2	2	5	3	1	4	1	2	2	1	2	5	2	2	2	2	2	2	2	2	5	2	2	2	1	5	2	2	2	5	5	2	2	2	2	2	2	5	2	5	2	2	2	4	5	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	3	2	2	1	1	1	1	3	2	1	2	1	1	1	5	5	1	1	1	4	1	1	1	5	1	1	1	5	1	5	4	4	5	5	2	3	3	2	3	1	1	1	3	5	2
43	90.7	178	28.6	1873	12	130	80	85	26.2	34.7	99	112	44	103.0000	19.00000	5.300000	221.0000	156.0000	0.800000	44.00000	5416.166	8400.000	5.37	16.90	47.4	88.3	31.8	36.1	257000.0	39.0	12.7	48.3	13.3	11.6	9.5	3	1	2	1	1	2	4	1	3	3	3	5	5	3	2	3	3	3	2	1	4	1	4	4	4	4	2	2	2	4	1	5	2	1	5	2	2	2	4	1	3	4	4	4	3	4	3	4	4	4	4	4	4	3	4	4	3	4	4	5	4	2	3	5	2	1	1	1	1	2	2	5	2	2	2	2	2	1	1	2	5	3	1	4	1	2	2	2	2	5	2	2	2	2	2	2	2	2	5	2	2	2	1	5	2	2	2	5	5	2	2	2	2	2	2	5	2	5	2	2	1	3	5	5	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	2	1	2	4	2	2	2	1	1	5	2	2	1	5	1	5	2	5	5	1	1	1	4	1	2	3	5	3	5	1	5	3	2	3	3	4	5	4	4	3	4	2	4	1	2	3	5	2
40	59.8	151	26.0	1235	7	140	80	70	39.7	25.4	93	102	33	98.0000	31.00000	4.200000	162.0000	156.0000	0.900000	38.00000	5416.166	7800.000	4.43	14.70	40.8	92.1	33.2	36.0	252000.0	38.8	15.5	45.7	12.4	11.0	9.1	3	2	4	1	1	3	3	1	4	3	3	1	5	1	4	5	5	2	1	2	1	1	1	1	4	1	2	5	4	2	4	1	3	4	2	2	2	2	2	3	1	1	2	2	1	1	2	2	2	2	1	1	1	2	1	1	2	3	1	3	2	2	3	5	2	1	1	1	1	2	2	5	2	2	2	2	2	2	2	2	5	3	1	4	1	2	2	2	2	5	2	2	2	2	2	2	2	2	5	2	2	1	1	2	2	1	2	5	5	2	2	2	2	2	2	5	2	5	2	2	2	4	5	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	4	1	2	2	3	2	1	2	3	5	1	5	5	1	1	1	4	1	4	3	5	2	4	1	5	3	3	2	4	2	5	2	2	2	3	2	1	1	5	4	5	2
23	52.0	160	20.0	1217	3	100	70	89	30.0	26.5	84	95	32	90.0000	34.00000	2.200000	185.0000	87.0000	0.700000	63.00000	108.600	6166.572	6400.00	4.51	13.1	39.1	86.7	29.0	33.5	306000.0	32.5	8.6	58.9	11.5	10.3	1	2	2	1	1	5	5	2	2	2	2	2	1	4	4	5	5	3	2	2	4	1	1	2	2	2	1	1	1	3	2	1	4	2	2	1	1	2	1	4	1	2	2	1	2	4	1	2	1	4	1	3	3	2	2	1	3	2	1	3	2	2	3	5	2	1	1	1	1	2	2	5	2	2	2	2	2	2	2	2	5	3	1	4	1	2	2	2	2	5	2	2	2	2	2	2	2	2	5	2	2	2	1	5	2	2	2	5	5	2	2	2	2	2	2	5	2	5	2	2	2	4	5	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	2	2	4	1	2	2	2	2	1	1	4	2	1	5	5	1	1	1	4	1	5	1	4	3	5	5	5	5	2	3	3	3	5	3	4	3	4	2	1	2	3	4	5	2
28	50.7	161	23.0	1285	4	130	80	75	34.4	27.4	76	98	34	106.4269	31.08092	4.495666	194.7283	162.1011	0.930414	48.76663	73.000	6166.572	5600.00	4.66	13.9	41.0	88.0	29.8	33.9	205000.0	42.4	9.4	48.2	12.0	14.1	1	2	5	1	1	4	2	4	3	3	2	2	1	4	4	5	5	3	2	2	4	1	1	2	2	2	1	1	2	3	2	1	3	2	2	1	1	2	1	3	1	2	2	1	2	3	1	2	1	3	1	1	1	2	2	1	3	2	1	3	2	2	3	5	2	1	1	1	1	2	2	5	2	2	2	2	2	2	2	2	5	3	1	4	1	2	2	2	2	5	2	2	2	2	2	2	2	2	5	2	2	2	1	5	2	2	2	5	5	2	2	2	2	2	2	5	2	5	2	2	2	4	5	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	2	2	4	1	2	2	2	2	1	1	4	2	1	5	5	1	1	1	4	1	5	1	4	3	5	5	5	5	2	3	3	3	5	3	4	3	4	2	1	2	3	4	5	2

0.3 dim.reduce

Use dim.reduce to visualize the continuous variables in 2 dimensions with tsne.

#dim.reduce(data[,1:36], method = "tsne") -> plt
#plt

0.4 ggm

Use ggm to construct a Gaussian Graphical Model with glasso and significance test method.

ggm(data[,1:36], significance = 0.05, rho = 0.1, community = TRUE, methods = c("glasso", "sin")) -> g
g$network

g

## $graph
## IGRAPH e5fcce4 UN-- 36 76 -- 
## + attr: label_1 (v/c), label_2 (v/c), name (v/c), weight_1 (e/n),
## | weight_2 (e/n)
## + edges from e5fcce4 (vertex names):
##  [1] PDW     --MPV      RDW     --MPV      Mxd     --Neut    
##  [4] Lymph   --RDW      Platelet--MPV      Platelet--Mxd     
##  [7] MCHC    --Mxd      MCH     --RDW      MCH     --Neut    
## [10] MCH     --Mxd      MCH     --Lymph    MCH     --Platelet
## [13] MCV     --Mxd      MCV     --Lymph    MCV     --MCHC    
## [16] Hct     --Mxd      Hct     --Platelet Hct     --MCHC    
## [19] HB      --RDW      HB      --MCHC     HB      --MCV     
## + ... omitted several edges
## 
## $betweenness
##         Age        Vazn        Ghad         BMI       MetaP     CharbiE 
## 267.9166667   9.9880952  94.7738095   6.6190476   3.5833333 108.8928571 
##     FesharS     FesharD        Nabz   DarsadChB   DarsadAzB    DorBadan 
##  33.0000000   0.0000000   0.0000000   6.7857143   0.3333333   6.0357143 
##    DorBasan   DorGardan         FSG        Urea         UAC        Chol 
##   0.8333333   8.2380952 273.0000000   0.0000000 143.5000000   0.0000000 
##          TG        Crea         LDH         CBC         WBC         RBC 
## 282.5000000  33.0000000 128.5000000   1.3095238  14.1666667  15.0000000 
##          HB         Hct         MCV         MCH        MCHC    Platelet 
##  16.5190476   6.9166667   5.4952381  38.3523810   6.4023810 124.7595238 
##       Lymph         Mxd        Neut         RDW         PDW         MPV 
##   6.1666667  22.4523810   1.1166667 121.8428571 283.5000000 273.5000000 
## 
## $network

0.5 bn

Use bn to learn the structure of a Bayesian network fitting data with si.hito.pc as a Constraint-based algorithm, mmhc as a Hybrid algorithm, and tabu as a Score-based algorithm. Repeat the bootstrap sampling 100 times. (very time consuming)

bn(data = data[,1:36], C.alg = c("si.hiton.pc"), S.alg = c("mmhc", "tabu"), blacklist = data.frame(to = "BMI", to = "Age"), R = 10, community = FALSE, str.thresh = 0.9, dir.thresh = 0.5) -> n
n$network

n

## $graph
##           from        to strength direction
## 5          Age   CharbiE      1.0 0.9000000
## 6          Age   FesharS      1.0 0.8000000
## 14         Age       FSG      1.0 0.3000000
## 38        Vazn       BMI      1.0 0.5500000
## 39        Vazn     MetaP      1.0 0.8500000
## 40        Vazn   CharbiE      1.0 0.9000000
## 46        Vazn  DorBadan      1.0 1.0000000
## 47        Vazn  DorBasan      1.0 1.0000000
## 79        Ghad DarsadChB      1.0 0.2000000
## 83        Ghad DorGardan      0.9 1.0000000
## 110        BMI   CharbiE      1.0 0.4000000
## 114        BMI DarsadChB      1.0 1.0000000
## 180    CharbiE     MetaP      1.0 0.8000000
## 181    CharbiE   FesharS      0.9 1.0000000
## 186    CharbiE  DorBadan      1.0 0.8000000
## 217    FesharS   FesharD      1.0 0.9000000
## 318  DarsadChB      Ghad      1.0 0.8000000
## 327  DarsadChB  DorBasan      1.0 1.0000000
## 355  DarsadAzB     MetaP      1.0 0.8000000
## 360  DarsadAzB DarsadChB      1.0 0.7000000
## 397   DorBadan  DorBasan      1.0 1.0000000
## 398   DorBadan DorGardan      1.0 1.0000000
## 462  DorGardan   FesharS      0.9 0.7777778
## 468  DorGardan  DorBasan      1.0 1.0000000
## 497        FSG   FesharS      1.0 0.9000000
## 526       Urea       Age      1.0 0.8000000
## 540       Urea       FSG      0.9 0.5555556
## 575        UAC       FSG      1.0 0.4500000
## 579        UAC      Crea      1.0 0.6500000
## 585        UAC       Hct      0.9 0.7777778
## 594        UAC       PDW      0.9 0.3888889
## 596       Chol       Age      1.0 1.0000000
## 613       Chol        TG      1.0 0.5500000
## 615       Chol       LDH      1.0 0.8000000
## 645         TG       FSG      1.0 0.7500000
## 647         TG       UAC      1.0 0.8500000
## 648         TG      Chol      1.0 0.4500000
## 650         TG       LDH      1.0 0.7000000
## 652         TG       WBC      0.9 1.0000000
## 675       Crea DarsadChB      1.0 0.7000000
## 681       Crea      Urea      1.0 0.8000000
## 690       Crea       Hct      0.8 0.8125000
## 699       Crea       PDW      0.8 0.3750000
## 827        RBC       CBC      1.0 0.6000000
## 832        RBC       MCH      1.0 0.3500000
## 835        RBC     Lymph      1.0 0.7500000
## 838        RBC       RDW      1.0 1.0000000
## 863         HB       WBC      1.0 1.0000000
## 865         HB       Hct      1.0 0.9500000
## 866         HB       MCV      1.0 0.2000000
## 941        MCV       Mxd      1.0 0.5500000
## 977        MCH      Neut      1.0 0.3000000
## 1117       Mxd      Neut      1.0 0.6000000
## 1143      Neut       WBC      1.0 0.8000000
## 1211       PDW       LDH      0.9 0.7222222
## 1225       PDW       MPV      1.0 0.8000000
## 1259       MPV       RDW      1.0 0.6000000
## 
## $network

0.6 min.forst

Employ min.forest with BIC to construct a mixed-interaction model fitting the data.

min.forest(data, stat = "BIC", community = TRUE) -> mf
mf$network

mf

## $summary
## gRapHD object
## Number of edges       = 1001
## Number of vertices    = 254
## Model                 = continuous 
## Statistic (minForest) = BIC
## Statistic (stepw)     = BIC
## Statistic (user def.) = 
## Edges (minForest)     = 1...253
## Edges (stepw)         = 254...1001
## Edges (user def.)     = 1...253
## 
## 
## $betweenness
##          Age        test1          Hct           HB          RBC 
## 5.020000e+02 1.139947e+04 1.549206e+00 2.680199e+02 2.589709e+02 
##        Lymph          MCH          RDW          Mxd          MCV 
## 0.000000e+00 1.520735e+02 1.714009e+02 2.930952e+00 4.196755e+02 
##         MCHC      test156      test155         Neut          CBC 
## 2.154365e+00 1.255000e+02 1.255000e+02 0.000000e+00 1.306940e+02 
##      test168      test169       test55       test56      test148 
## 1.006624e+01 2.107556e+02 1.160397e+04 0.000000e+00 1.678333e+02 
##      test147     Platelet      test115      test118        test9 
## 0.000000e+00 0.000000e+00 1.369430e+03 0.000000e+00 8.471903e+01 
##      FesharS      test107      test109          WBC     DorBadan 
## 1.922778e+02 2.469405e+02 4.354875e+01 1.428571e-01 7.078053e+02 
##     DorBasan      test121      test123          BMI       test11 
## 2.324816e+02 5.501543e+02 4.008822e+01 2.901087e+02 0.000000e+00 
##       test89       test90       test67       test66      CharbiE 
## 1.916419e+03 0.000000e+00 2.115667e+02 3.650000e+00 7.975781e+02 
##          PDW          MPV      test116         Vazn    DarsadChB 
## 3.105380e+02 1.404977e+02 3.333333e-01 1.338376e+03 1.048889e+03 
##       test10      FesharD    DarsadAzB       test23       test21 
## 3.333333e-01 0.000000e+00 3.068576e+03 5.781621e+02 0.000000e+00 
##       test93       test96       test62       test64        test2 
## 2.333333e+00 0.000000e+00 2.390799e+03 5.372167e+02 4.499763e+03 
##      test102      test104         Crea          UAC      test131 
## 7.623790e+02 0.000000e+00 1.489056e+02 5.864777e+02 3.333333e-01 
##      test130      test184      test183       test80       test81 
## 0.000000e+00 6.665271e+02 1.229430e+01 2.211805e+03 2.250000e+00 
##       test71       test72      test182       test45       test43 
## 7.059152e+02 0.000000e+00 1.883781e+02 4.001539e+02 4.107369e+02 
##       test61       test59       test94        MetaP      test154 
## 5.506635e+02 2.937909e+03 6.132332e+02 3.633254e+00 0.000000e+00 
##       test60       test47       test52       test57      test108 
## 5.934584e+03 8.536685e+01 1.233075e+03 1.205387e+03 0.000000e+00 
##       test54    DorGardan        test5        test4       test58 
## 2.530774e+03 1.870166e+03 0.000000e+00 1.217121e+01 1.236246e+03 
##      test138      test139      test167      test166         Ghad 
## 6.733333e+00 1.776500e+02 4.189404e+01 0.000000e+00 5.141695e+00 
##        test6       test98      test100       test75       test76 
## 5.445673e+02 1.960000e+03 0.000000e+00 2.520000e+02 0.000000e+00 
##       test44       test53      test124       test49       test41 
## 5.423093e+01 1.300473e+02 0.000000e+00 0.000000e+00 1.804463e+03 
##      test161      test160      test152      test150       test33 
## 0.000000e+00 0.000000e+00 1.246000e+03 0.000000e+00 3.050347e+01 
##       test40      test203      test204      test111      test113 
## 3.256479e+01 5.538252e+02 7.686407e+01 4.932429e+03 0.000000e+00 
##      test105         Urea      test122       test95      test172 
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 8.094780e+02 
##       test46      test202       test35      test171      test170 
## 0.000000e+00 2.903914e+02 1.047044e+03 9.824804e+00 1.886648e+01 
##      test132      test158      test159       test38       test74 
## 2.523333e+02 1.606107e+01 0.000000e+00 5.227475e+02 0.000000e+00 
##      test205       test39       test99       test36      test103 
## 7.537114e+01 9.666856e+01 1.482000e+03 2.006977e+03 0.000000e+00 
##       test51       test50       test65       test48       test34 
## 3.615653e+03 3.358870e+01 9.066667e+00 5.059600e+02 0.000000e+00 
##      test146       test42       test25      test142      test143 
## 3.351667e+02 1.926726e+01 2.746363e+00 2.932883e+03 1.575483e+03 
##      test185      test162      test163      test198       test85 
## 2.520000e+02 5.020000e+02 5.981757e+02 6.106198e+02 3.078938e+01 
##      test164      test165       test37      test112       test19 
## 2.520000e+02 0.000000e+00 0.000000e+00 4.872000e+03 6.129036e+01 
##      test127      test128      test178      test177       test83 
## 1.691167e+02 0.000000e+00 3.225541e+01 3.731129e+00 2.483333e+00 
##      test201       test18      test125           TG         Chol 
## 0.000000e+00 1.820096e+02 5.791127e+01 0.000000e+00 0.000000e+00 
##      test151          FSG      test206       test69      test134 
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.813500e+02 
##      test135       test24       test12       test13       test92 
## 1.065900e+03 2.049665e+02 1.153059e+02 0.000000e+00 1.441131e+03 
##       test88       test26      test186      test117      test119 
## 0.000000e+00 0.000000e+00 1.798380e+02 0.000000e+00 1.593870e+02 
##       test78       test14       test16       test22      test110 
## 0.000000e+00 2.610661e+02 1.048874e+01 4.343678e+02 0.000000e+00 
##       test15       test17        test3      test174      test120 
## 2.046063e+02 0.000000e+00 2.978758e+02 2.236597e+03 0.000000e+00 
##       test68       test84      test215      test173      test216 
## 2.500000e-01 2.066667e+00 5.000000e-01 5.611173e+01 1.357381e+02 
##       test82      test218      test213      test194      test190 
## 0.000000e+00 2.879051e+02 5.902931e+01 4.651234e+02 2.162900e+02 
##      test136      test211          LDH       test87      test106 
## 6.250000e+00 4.520202e-01 2.151969e+02 0.000000e+00 2.000000e+00 
##      test175      test176      test180       test30       test29 
## 2.520000e+02 0.000000e+00 1.324505e+02 5.062534e+00 3.611111e-01 
##      test144      test199      test140      test126      test197 
## 0.000000e+00 6.752106e+01 8.008333e+01 8.283333e+01 6.592891e+02 
##       test70       test91      test208       test73      test200 
## 6.976161e+01 2.426914e+01 1.173391e+02 0.000000e+00 5.380347e+02 
##       test63       test28      test193      test192      test179 
## 3.333333e-01 4.832816e+02 3.333333e-01 1.166667e+00 0.000000e+00 
##      test101      test209      test188      test189      test191 
## 1.960919e+02 0.000000e+00 1.158810e+03 6.541991e+01 0.000000e+00 
##       test79       test20      test207       test97       test31 
## 2.120539e+02 0.000000e+00 0.000000e+00 1.371001e+02 0.000000e+00 
##       test32      test114      test196      test195      test212 
## 0.000000e+00 3.778014e+02 1.607576e+00 0.000000e+00 3.940397e+00 
##      test210       test86      test217      test145       test77 
## 0.000000e+00 1.416667e+00 0.000000e+00 7.863333e+02 5.190811e+01 
##        test8      test214      test137      test153        test7 
## 0.000000e+00 0.000000e+00 2.066667e+02 0.000000e+00 0.000000e+00 
##      test141      test187      test133       test27      test181 
## 1.482000e+03 5.694024e+02 0.000000e+00 0.000000e+00 0.000000e+00 
##         Nabz      test149      test157      test129 
## 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 
## 
## $network

0.7 div

Use div with 1000 times of permutation to find the most deferentially answered questions between people with answer “1” to the test 62 and people with answer “2” to the test 62. (Test 62 is about cardiovascular diseases.)

g1 <- which(data$test62 == "1") 
g2 <- which(data$test62 == "2")
div(data[37:dim(data)[2]], g1, g2, permute = 1000) -> KL
KL$name <- row.names(KL)
KL <- KL %>% arrange(desc(KL))
kableExtra::kable(head(KL,n = 20)) %>%
  kable_styling() %>%
  scroll_box(width = "400px", height = "400px")

KL	p.value	name
15.7933761	0.01	test62
4.6857327	0.01	test65
4.2716551	0.01	test63
3.6990622	0.01	test64
2.2007322	0.01	test67
1.8612079	0.01	test66
0.9330294	0.01	test168
0.9090498	0.01	test1
0.8622886	0.01	test169
0.6740090	0.01	test69
0.5740225	0.01	test71
0.5268477	0.01	test174
0.4537840	0.01	test68
0.4197713	0.01	test54
0.4043018	0.01	test57
0.3865165	0.01	test178
0.3788912	0.01	test184
0.3759118	0.01	test73
0.3699828	0.01	test89
0.3543039	0.01	test80

0.8 div2

Use div2 to find the most deferentially answered questions between people with relatively high BMI (relative to blood pressure) and people with high pressure (relative to BMI). (FesharS demonstrates blood pressure.)

div2(data[37:dim(data)[2]], var1 = data$BMI, var2 = data$FesharS, permute = 0) -> KL
KL$name <- row.names(KL)
KL <- KL %>% arrange(desc(KL))
kableExtra::kable(head(KL, n = 20)) %>%
  kable_styling() %>%
  scroll_box(width = "300px", height = "400px")

KL	name
5.8395634	test11
0.7040959	test1
0.5497792	test2
0.4742267	test8
0.4487907	test172
0.3703050	test89
0.3502208	test174
0.3422061	test121
0.3240845	test78
0.3101447	test56
0.3010604	test55
0.2995396	test71
0.2895905	test178
0.2648403	test57
0.2594900	test124
0.2542359	test6
0.2417774	test74
0.2400237	test80
0.2140980	test91
0.2092262	test195

0.9 plot

Use plot for different visualizations. levels is set to 10 to capture categorical variables.

Histogram for test 10:

plot(data, vars = c("test10"), levels = 10)

Density plot for BMI:pl

plot(data, vars = c("BMI"), levels = 10)

Boxplot of BMI for different answers of test 1:

plot(data, vars = c("test1", "BMI"), levels = 10)

Relative histogram of test 1 vs test 10:

plot(data, vars = c("test1", "test10"), levels = 10)

Scatter plot of Vazn (weight) vs BMI:

plot(data, vars = c("Vazn", "BMI"), levels = 10)

Pie chart of test 1:

plot(data, vars = c("test1"), levels = 10, pie = TRUE)